Intelligent wireless communication anti-interference technology based on machine learning

Wireless communication technology faces challenges in terms of security and efficiency, and there is an urgent need to adopt intelligent methods to counter interference and improve the wireless communication experience in different interference environments. Therefore, a resistance-to-interference technology has been proposed to address the communication problems faced by users in different scenarios. Experimental results show that the multi-user distributed interference resistance alliance formation (MDACF) algorithm, compared with direct transmission, has a transmission efficiency increase of 12%. The interference resistance algorithm based on offline UCB1 adjustment of Q-learning (DUCBQ) can find the path with the minimum hop count in approximately 25 iterations, and the proximal policy optimization (PPO) algorithm, after training, has an average data packet reception rate of approximately 0.91. The average performance estimation value of the intelligent interference resistance technology proposed in the study is 0.8435. The three intelligent interference resistance algorithms proposed in the study have feasibility and interference resistance capabilities when facing interference environments.